Tucker Carrington Jr.

Atomic and Molecular Physics, and Optics · 2002–2015

A researcher in the Science corpus: 10 papers; trusts 15 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Atomic and Molecular Physics, and Optics — 36 citations into it; #833 of 7,135 by trust here, #1,278 by citations
• Materials Chemistry — 19 citations into it; #1,744 of 11,513 by trust here, #4,806 by citations
• Spectroscopy — 14 citations into it; #585 of 1,674 by trust here, #929 by citations
• Physical and Theoretical Chemistry — 12 citations into it
• Molecular Biology — 8 citations into it; #7,239 of 17,773 by trust here, #10,193 by citations
• Statistical and Nonlinear Physics — 8 citations into it
• Scaling & Learning Theory — 7 citations into it
• Model Architectures — 6 citations into it

Whom Tucker Carrington Jr. trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Sergei Manzhos
2. Gene H. Golub
3. Kurt Hornik
4. Genyuan Li
5. R. Lehoucq
6. Ken-ichi Funahashi
7. J. Behler
8. Bernard Widrow
9. Martin H. Anthony

Who trusts Tucker Carrington Jr. overall

1. J. Behler
2. B. A. Le
3. Sergei Manzhos
4. Julien Yvonnet
5. Pavlo O. Dral
6. Qi-Chang He
7. Kristof T. Schütt
8. Oliver T. Unke
9. Hans-Dieter Meyer
10. Kun Yao

Papers from Tucker Carrington Jr.'s perspective

Ordered by how strongly the researchers Tucker Carrington Jr. trusts endorse each paper. Same corpus, a different order for every researcher.

1. Matrix computations
2. ARPACK users' guide - solution of large-scale eigenvalue problems with implicitly restarted Arnoldi methods
3. Pattern Recognition and Machine Learning
4. Neural Networks for Pattern Recognition
5. ImageNet classification with deep convolutional neural networks
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. The Nature of Statistical Learning Theory
8. Learning Multiple Layers of Features from Tiny Images
9. The Elements of Statistical Learning
10. Gradient-based learning applied to document recognition

Their papers

• A random-sampling high dimensional model representation neural network for building potential energy surfaces. (2006) — 254 citations
• Neural network‐based approaches for building high dimensional and quantum dynamics‐friendly potential energy surfaces (2015) — 210 citations
• Discrete‐Variable Representations and their Utilization (2007) — 511 citations
• A nested molecule-independent neural network approach for high-quality potential fits. (2006) — 176 citations
• Using neural networks to represent potential surfaces as sums of products. (2006) — 162 citations
• Using redundant coordinates to represent potential energy surfaces with lower-dimensional functions. (2007) — 97 citations
• Using neural networks, optimized coordinates, and high-dimensional model representations to obtain a vinyl bromide potential surface. (2008) — 105 citations
• Variational quantum approaches for computing vibrational energies of polyatomic molecules (2008) — 446 citations
• A contracted basis-Lanczos calculation of vibrational levels of methane: Solving the Schrödinger equation in nine dimensions (2003) — 172 citations
• A general framework for discrete variable representation basis sets (2002) — 138 citations